Frontiers in Aging Neuroscience (Sep 2021)
Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women With Alzheimer’s Disease
Abstract
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (APOE ε4) AD risk compared to males, molecular signatures underlying these differences remain elusive.Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer’s disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD.Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the APOE ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD patients. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males.Conclusion: These results help identify sex and APOE ε4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.
Keywords
